Lifeboat Foundation

Can machine learning predict chaos? This paper performs a large-scale comparison of modern forecasting methods on a giant dataset of 135 chaotic systems.

Chaos and unpredictability are traditionally synonymous, yet large-scale machine-learning methods recently have demonstrated a surprising ability to forecast chaotic systems well beyond typical predictability horizons. However, recent works disagree on whether specialized methods grounded in dynamical systems theory, such as reservoir computers or neural ordinary differential equations, outperform general-purpose large-scale learning methods such as transformers or recurrent neural networks. These prior studies perform comparisons on few individually chosen chaotic systems, thereby precluding robust quantification of how statistical modeling choices and dynamical invariants of different chaotic systems jointly determine empirical predictability.

McGill University researchers have made a breakthrough in diagnostic technology, inventing a ‘lab on a chip’ that can be 3D-printed in just 30 minutes. The chip has the potential to make on-the-spot testing widely accessible.

As part of a recent study, the results of which were published in the journal Advanced Materials, the McGill team developed capillaric chips that act as miniature laboratories.

Unlike other computer microprocessors, these chips are single-use and require no external power source — a simple paper strip suffices.

Elon Musk is using what Tesla knows about self-dr…’

Future batteries could charge up by relying on a quantum effect known as indefinite causal order, whereby the laws of cause and effect are scrambled and power can move through the system quicker.

Scientists may have found a more efficient water to desalinate water using solar power, according to new research, offering a solution for global water scarcity through the use of renewable energy.

Researchers at Nankai University in Tianjin, China, developed the concept of a solar-powered desalination system that produces fresh water by using smart DNA hydrogels that does not consume additional energy, compared to conventional desalination strategies currently in use, such as reverse osmosis, which use copious amounts of energy, according to a paper published in the journal Science Advances on Thursday.

The same process can be used simultaneously to extract uranium from seawater or treat uranyl containing nuclear wastewater, the researchers said.

It’s another reminder to always search through that museum drawer.

When it comes to delivering drugs to the body, a major challenge is ensuring that they remain in the area they’re treating and continuing to deliver their payload accurately. While major strides have been made in delivering drugs, monitoring them is a challenge that often requires invasive procedures like biopsies.

Researchers at NYU Tandon led by Jin Kim Montclare, Professor of Chemical and Biomolecular Engineering, have developed proteins that can assemble themselves into fibers to be used as therapeutic agents for the potential treatments of multiple diseases.

These biomaterials can encapsulate and deliver therapeutics for a host of diseases. But while Montclare’s lab has long worked on producing these materials, there was once a challenge that was hard to overcome—how to make sure that these proteins continued to deliver their therapeutics at the correct location in the body for the necessary amount of time.

A recent study by Carnegie Mellon University (CMU) shows that Google’s latest large language model, Gemini Pro, lags behind GPT-3.5 and far behind GPT-4 in benchmarks.

The results contradict the information provided by Google at the Gemini presentation. They highlight the need for neutral benchmarking institutions or processes.

Gemini Pro loses out to GPT-3.5 in benchmarks.

A non-organic intelligent system has for the first time designed, planned and executed a chemistry experiment, Carnegie Mellon University researchers report in the journal Nature (“Autonomous chemical research with large language models”).